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PubMedQA

Download

PQA-L is already in ./data/

PQA-U

PQA-A

Split the dataset

After downloading PQA-A and PQA-L as ori_pqaa.json and ori_pqau.json in the ./data/, enter the ./preprocess/ directory and split the dataset:

cd preprocess
python split_dataset.py pqaa
python split_dataset.py pqal

Please be aware that there is no offical code for splitting PQA-U.

Evaluation and submission

To evaluate your model predictions, please prepare the results in a json format where the key is PMID and value is one of "yes", "no", and "maybe". Run the following script to get the performance:

python evaluation.py PREDICTIONS_PATH

To submit a system on the Leaderboard, please send an email that contains the model predictions and a brief description of the system to Qiao Jin via qiaojin.andy@gmail.com.

Human performance

After splitting the PQA-L and having ./data/test_set.json, one can run the following script to get human performance:

python get_human_performance.py

Citation

If you use PubMedQA in your research, please cite our paper by:

@inproceedings{jin2019pubmedqa,
  title={PubMedQA: A Dataset for Biomedical Research Question Answering},
  author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  pages={2567--2577},
  year={2019}
}

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PubMedQA: A Dataset for Biomedical Research Question Answering

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